Call:
lm(formula = age_stop ~ mom_age, data = bf)
Coefficients:
(Intercept) mom_age
5.920 0.389
2024-09-17
Call:
lm(formula = age_stop ~ mom_age, data = bf)
Coefficients:
(Intercept) mom_age
5.920 0.389
# A tibble: 5 × 2
mom_age age_stop
<dbl> <dbl>
1 20 5
2 20 29
3 20 6
4 20 NA
5 20 12
Predicted age_stop = 5.92 + 0.389*20 = 13.7
# A tibble: 1 × 2
mom_age .fitted
<dbl> <dbl>
1 20 13.7
# A tibble: 4 × 5
.rownames mom_age age_stop .fitted .resid
<chr> <dbl> <dbl> <dbl> <dbl>
1 8 20 5 13.7 -8.70
2 40 20 29 13.7 15.3
3 44 20 6 13.7 -7.70
4 67 20 12 13.7 -1.70
Analysis of Variance Table
Response: age_stop
Df Sum Sq Mean Sq F value Pr(>F)
mom_age 1 570.0 569.99 5.7531 0.01879 *
Residuals 80 7925.9 99.07
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
[1] 0.06708949
2.5 % 97.5 %
(Intercept) -3.19546976 15.035265
mom_age 0.06625878 0.711827
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.92 4.58 1.29 0.200
2 mom_age 0.389 0.162 2.40 0.0188